Representing images with simple and robust features is a crucial step in image processing, computer vision, and machine learning. Traditional feature extraction approaches such as scale-invariant feature transform (SIFT) are time-consuming, expensive, and domain specific.
Applying deep learning techniques to image classification and pattern recognition is a promising approach. Deep learning algorithms model high-level abstractions of data by using multiple processing layers with complex structures. However, even after the initial training period, applying deep learning algorithms can be computationally expensive, particularly for real-time applications.